The following abbreviations are herewith defined, at least some of which are referred to within the following description of the present disclosure.
IDEA Interactive Data Exploration and Analytics
RAM Random Access Memory
VoD Video on Demand
VM Virtual Machine
Current on-demand services such as Netflix, Spotify, MediaFirst and NuVu present a user with recommendations for consuming media. Recommendations are a way to present a sizeable catalog to the user. In the past, generating a set of recommendations typically resulted in using a non-interactive browsing mode which resulted in displaying a set of recommendations for the user. This, non-interactive browsing mode of displaying a set of recommendations as a browsing pattern for the user while being a good start, was not a complete solution and has been recognized as such in the art. A more detailed discussion about the non-interactive browsing mode and problems associated therewith is provided in H. Steck et al. “Interactive Recommender Systems,” in RecSys, Vienna, 2015 (the contents of which are hereby incorporated herein by reference).
A promising solution to improve upon the non-interactive browsing mode is to provide some form of user interactive recommendations (user agency), where a next set of recommendations which is displayed for a user is a result of the user's actions. A more detailed discussion about user interactive recommendations (user agency) is provided in C. Johnson, “Interactive Recommender Systems with Netflix and Spotify,” 16 Sep. 2015 see http://www.slideshare.net/MrChrisJohnson/interactive-recommender-systems-with-netflix-and-spotify/5-What_are_Interactive_RSsDefine_InteractiveInfluencing [Accessed 7 Jun. 2016] (the contents of which are hereby incorporated herein by reference). The user interactive mode in which the user is highly engaged with the recommendations requires the recommendation system to react quickly with providing relevant recommendations for the user in order to retain and inspire future user engagement. There have been many attempts to provide some form of user interactive recommendations (user agency), including: Y. Krishnamurthy et al. “Interactive Exploration for Domain Discovery on the Web” KDD 2016 Workshop on Interactive Data Exploration and Analytics (IDEA '16), Aug. 4, 2016, San Francisco, Calif., USA, 8 pages; Proceedings of the ACM SIGKDD 2017 Full-Day Workshop on Interactive Data Exploration and Analytics (IDEA 2017), Halifax, Nova Scotia, Canada, Aug. 14, 2017, 85 pages; U.S. Patent Publication No. 2016/0019217; U.S. Patent Publication No. 2016/0037227; and U.S. Patent Publication No. 2010/0161619 (the contents of these documents are hereby incorporated herein by reference).
This prior art all acknowledges the difficulty in generating recommendations to users, and the need to include user agency in the user interactive process. Further, this prior art all has the same substantive problem in that they do not properly address user agency versus serendipity. In short, when a user looks for a recommendation, they can express their desire in terms of a feeling, yet the prior art methods described above search for either an explicit tag, or a related tag. By performing this substitution, the user must perform many actions, such as adding a plurality of tags, before they reach a desired outcome. As a result, these prior art recommendation systems tend to become more of a search engine than a recommendation engine.
This weakness of the prior art recommendation systems is exemplified in U.S. Pat. No. 9,141,694 (the contents of which are incorporated herein by reference) which discloses that it may be desirable to use (apply) media items themselves in the search for the set of recommendations. What they do, then, is extract a tag cloud from the given item, and then use this tag cloud to provide the recommendations. This process has the same pitfalls as the above user interactive processes, including the reliance on the accuracy of the tags themselves.
There has also been research on using tags and interactive user settings to tags and/or labels which allows users to mutate the recommendations list. However, these approaches put limits on what the tags/attributes mean and what is investigated instead is a means of varying the weight(s) of the tags/attributes. This research has been discussed in following documents: (1) J. Solomon, “Heterogeneity in Customization of Recommender Systems By Users with Homogenous Preferences,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016; and (2) B. P. Knijenburg et al. “Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems,” in Proceedings of the fifth ACM conference on Recommender systems, 2011 (the contents of these documents are hereby incorporated herein by reference). Hence, it can be appreciated that there is a need to address the aforementioned problems. This need and other needs are addressed herein by the present disclosure.